Random survival forests: quantifying uncertainties and other extensions

One of biomedical studies' most commonly encountered problems is analyzing censored survival data. Survival analysis differs from standard regression problems by one central feature: the event of interest may not be fully observed. Therefore, we must adapt the statistical methods used to analyze this data to handle the missing information. In the first chapter, we briefly introduce right-censored survival data and introduce survival random forest models for analyzing them. In addition to the statistical formulation, we provide details of tuning parameters commonly considered in practice. In chapter 2, this thesis proposes a method for statistical inference on cumulative hazard predictions by extending recent developments in infinite-order incomplete U-statistics. Before our work, there was no methodology for calculating a confidence band for a survival random forest prediction. We introduce numerical methods for estimating a cumulative hazard prediction over the observed failure times and a covariance matrix of the predictions at each failure time. Then, using the covariance matrix and assuming a Gaussian distribution, we find a critical value to use for building a confidence band around the prediction. We show that the confidence bands contain the average random forest prediction at least 95% of the time in simulations and give an example with an actual data set. In chapter 3, we introduce a method for statistical inference on variable importance estimates from a survival random forest. Previous work on this topic was primarily focused on regression random forests, with some work on survival random forests. We outline variable importance estimation and the associated variance estimation using similar concepts to the survival predictions. We then use those estimates to build a confidence interval. We show through simulations that these intervals cover the average random forest variable importance at least 93% which improves over the competing method at 84%. In chapter 4, we propose new random survival forests that utilize information from existing studies to improve the model fitting. Random survival forests are popular statistical models in biomedical studies, especially for cancer studies with high-dimensional genetic information. With the abundance of cancer genetics and genomics data, new studies can borrow information from existing ones. This incorporation is achieved by constructing a new type of splitting rule that penalizes the marginal scores of a potential split so that variables with strong existing known association with the outcome are encouraged to be selected. We experimented with this penalized random survival forest by utilizing two types of prior information: the marginal p-value, which is often released from existing studies, and the variable importance measure calculated from the existing data if the complete data are available. We perform simulation studies to demonstrate the performance over existing single data set approaches and apply our method to the TCGA GBM and LGG data to discover brain tumor biomarkers.Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo termsThe student, Sarah Formentini, accepted the attached license on 2022-07-12 at 22:39.The student, Sarah Formentini, submitted this Dissertation for approval on 2022-07-12 at 22:47.This Dissertation was approved for publication on 2022-07-13 at 12:51.DSpace SAF Submission Ingestion Package generated from Vireo submission #18275 on 2022-11-15 at 18:20:4

Risk of COVID-19 after natural infection or vaccinationResearch in context

Summary: Background: While vaccines have established utility against COVID-19, phase 3 efficacy studies have generally not comprehensively evaluated protection provided by previous infection or hybrid immunity (previous infection plus vaccination). Individual patient data from US government-supported harmonized vaccine trials provide an unprecedented sample population to address this issue. We characterized the protective efficacy of previous SARS-CoV-2 infection and hybrid immunity against COVID-19 early in the pandemic over three-to six-month follow-up and compared with vaccine-associated protection. Methods: In this post-hoc cross-protocol analysis of the Moderna, AstraZeneca, Janssen, and Novavax COVID-19 vaccine clinical trials, we allocated participants into four groups based on previous-infection status at enrolment and treatment: no previous infection/placebo; previous infection/placebo; no previous infection/vaccine; and previous infection/vaccine. The main outcome was RT-PCR-confirmed COVID-19 >7–15 days (per original protocols) after final study injection. We calculated crude and adjusted efficacy measures. Findings: Previous infection/placebo participants had a 92% decreased risk of future COVID-19 compared to no previous infection/placebo participants (overall hazard ratio [HR] ratio: 0.08; 95% CI: 0.05–0.13). Among single-dose Janssen participants, hybrid immunity conferred greater protection than vaccine alone (HR: 0.03; 95% CI: 0.01–0.10). Too few infections were observed to draw statistical inferences comparing hybrid immunity to vaccine alone for other trials. Vaccination, previous infection, and hybrid immunity all provided near-complete protection against severe disease. Interpretation: Previous infection, any hybrid immunity, and two-dose vaccination all provided substantial protection against symptomatic and severe COVID-19 through the early Delta period. Thus, as a surrogate for natural infection, vaccination remains the safest approach to protection. Funding: National Institutes of Health

Implementation of a Brazilian Cardioprotective Nutritional (BALANCE) Program for improvement on quality of diet and secondary prevention of cardiovascular events: A randomized, multicenter trial

Background: Appropriate dietary recommendations represent a key part of secondary prevention in cardiovascular disease (CVD). We evaluated the effectiveness of the implementation of a nutritional program on quality of diet, cardiovascular events, and death in patients with established CVD. Methods: In this open-label, multicenter trial conducted in 35 sites in Brazil, we randomly assigned (1:1) patients aged 45 years or older to receive either the BALANCE Program (experimental group) or conventional nutrition advice (control group). The BALANCE Program included a unique nutritional education strategy to implement recommendations from guidelines, adapted to the use of affordable and regional foods. Adherence to diet was evaluated by the modified Alternative Healthy Eating Index. The primary end point was a composite of all-cause mortality, cardiovascular death, cardiac arrest, myocardial infarction, stroke, myocardial revascularization, amputation, or hospitalization for unstable angina. Secondary end points included biochemical and anthropometric data, and blood pressure levels. Results: From March 5, 2013, to Abril 7, 2015, a total of 2534 eligible patients were randomly assigned to either the BALANCE Program group (n = 1,266) or the control group (n = 1,268) and were followed up for a median of 3.5 years. In total, 235 (9.3%) participants had been lost to follow-up. After 3 years of follow-up, mean modified Alternative Healthy Eating Index (scale 0-70) was only slightly higher in the BALANCE group versus the control group (26.2 ± 8.4 vs 24.7 ± 8.6, P <.01), mainly due to a 0.5-serving/d greater intake of fruits and of vegetables in the BALANCE group. Primary end point events occurred in 236 participants (18.8%) in the BALANCE group and in 207 participants (16.4%) in the control group (hazard ratio, 1.15; 95% CI 0.95-1.38; P =.15). Secondary end points did not differ between groups after follow-up. Conclusions: The BALANCE Program only slightly improved adherence to a healthy diet in patients with established CVD and had no significant effect on the incidence of cardiovascular events or death. © 2019 The Author